Smart metrology on microscope images

ABSTRACT

Smart metrology methods and apparatuses disclosed herein process images for automatic metrology of desired features. An example method at least includes extracting a region of interest from an image, the region including one or more boundaries between different sections, enhancing at least the extracted region of interest based on one or more filters, generating a multi-scale data set of the region of interest based on the enhanced region of interest, initializing a model of the region of interest; optimizing a plurality of active contours within the enhanced region of interest based on the model of the region of interest and further based on the multi-scale data set, the optimized plurality of active contours identifying the one or more boundaries within the region of interest, and performing metrology on the region of interest based on the identified boundaries.

This Application is a continuation of U.S. patent application Ser. No.16/177,034, filed Oct. 31, 2018.

FIELD OF THE INVENTION

The techniques disclosed herein are generally related to implementingsmart metrology using images, and more specifically related toperforming smart metrology on images obtained with a charged particlemicroscope.

BACKGROUND OF THE INVENTION

Metrology of features in images can be a difficult and lengthy process,especially on images obtained by a charged particle microscope. Thedifficulty and lengthy process may be due in part to noisy images thatautomatic imaging processing algorithms have difficulty processing,which leads to user manipulation and multiple steps. This may not be anissue when there are only a few images to analyze. However, in amanufacturing environment, such as the semiconductor industry, wherelarge numbers of samples are imaged and require analysis, this processtruly slows down needed analysis.

While some improvements to image processing have occurred over the yearsleading to improved metrology accuracy and efficiency, theseimprovements are not enough in today's semiconductor manufacturingclimate, e.g., node size and throughput. As such, improvements to imageprocess and metrology automation are desired across an industry.

SUMMARY

Smart metrology methods and apparatuses disclosed herein process imagesfor automatic metrology of desired features. An example method at leastincludes extracting a region of interest from an image, the regionincluding one or more boundaries between different sections, enhancingat least the extracted region of interest based on one or more filters,generating a multi-scale data set of the region of interest based on theenhanced region of interest, initializing a model of the region ofinterest; optimizing a plurality of active contours within the enhancedregion of interest based on the model of the region of interest andfurther based on the multi-scale data set, the optimized plurality ofactive contours identifying the one or more boundaries within the regionof interest, and performing metrology on the region of interest based onthe identified boundaries.

Another embodiment includes non-transitory computer readable mediumincluding code that, when executed by one or more processors, causes theone or more processors to extract a region of interest from an image,the region including one or more boundaries between different sectionsof the region of interest; enhance at least the extracted region ofinterest based on one or more filters; generate a multi-scale data setof the region of interest based on the enhanced region of interest;initialize a model of the region of interest, the initialized modeldetermining at least first and second bounds of the region of interest;optimize a plurality of active contours within the enhanced region ofinterest based on the model of the region of interest and further basedon the multi-scale data set, the optimized plurality of active contoursidentifying the one or more boundaries within the region of interest;and perform metrology on the region of interest based on the identifiedboundaries.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is an example image sequence showing image processing andresulting metrology in accordance with an embodiment of the presentdisclosure.

FIG. 2 is an example image sequence showing image processing inaccordance with an embodiment of the present disclosure.

FIG. 3 is an example method for processing an image and performingmetrology on one or more features within the image in accordance with anembodiment of the present disclosure.

FIG. 4 is a method for optimizing a plurality of active contours withinan ROI in accordance with an embodiment of the present disclosure.

FIG. 5 is an example image sequence in accordance with an embodiment ofthe present disclosure.

FIG. 6 is an example method in accordance with an embodiment of thepresent disclosure.

FIG. 7 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 8 is an example charged particle microscope environment forperforming at least part of the methods disclosed herein and inaccordance with an embodiment of the present disclosure.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention relate to smart metrology onmicroscope images. In some examples, the images are processed to enhancedesired features then active contours are optimized to locate boundariesformed at feature interfaces. The active contours then become anchorsfor performing accurate metrology on the features. However, it should beunderstood that the methods described herein are generally applicable toa wide range of different AI enhanced metrology, and should not beconsidered limiting.

As used in this application and in the claims, the singular forms “a,”“an,” and “the” include the plural forms unless the context clearlydictates otherwise. Additionally, the term “includes” means “comprises.”Further, the term “coupled” does not exclude the presence ofintermediate elements between the coupled items. Additionally, in thefollowing discussion and in the claims, the terms “including” and“comprising” are used in an open-ended fashion, and thus should beinterpreted to mean “including, but not limited to . . . ” The term“integrated circuit” refers to a set of electronic components and theirinterconnections (internal electrical circuit elements, collectively)that are patterned on the surface of a microchip. The term“semiconductor device” refers generically to an integrated circuit (IC),which may be integral to a semiconductor wafer, separated from a wafer,or packaged for use on a circuit board. The term “FIB” or “focused ionbeam” is used herein to refer to any collimated ion beam, including abeam focused by ion optics and shaped ion beams.

The systems, apparatus, and methods described herein should not beconstrued as limiting in any way. Instead, the present disclosure isdirected toward all novel and non-obvious features and aspects of thevarious disclosed embodiments, alone and in various combinations andsub-combinations with one another. The disclosed systems, methods, andapparatus are not limited to any specific aspect or feature orcombinations thereof, nor do the disclosed systems, methods, andapparatus require that any one or more specific advantages be present orproblems be solved. Any theories of operation are to facilitateexplanation, but the disclosed systems, methods, and apparatus are notlimited to such theories of operation.

Although the operations of some of the disclosed methods are describedin a particular, sequential order for convenient presentation, it shouldbe understood that this manner of description encompasses rearrangement,unless a particular ordering is required by specific language set forthbelow. For example, operations described sequentially may in some casesbe rearranged or performed concurrently. Moreover, for the sake ofsimplicity, the attached figures may not show the various ways in whichthe disclosed systems, methods, and apparatus can be used in conjunctionwith other systems, methods, and apparatus. Additionally, thedescription sometimes uses terms like “produce” and “provide” todescribe the disclosed methods. These terms are high-level abstractionsof the actual operations that are performed. The actual operations thatcorrespond to these terms will vary depending on the particularimplementation and are readily discernible by one of ordinary skill inthe art.

The smart metrology techniques disclosed herein enable automaticmetrology of features in images. The images may be obtained with chargedparticle microscopes (SEM, TEM, STEM, FIB, etc.) or optical microscopes,to provide a couple of examples. The images may typically include one ormore features, e.g., regions of interest, and it is desired to determinesizes of at least the one or more features. The smart metrology mayperform measurements based on a plurality of image processing algorithmsin conjunction with active contours, for example. The active contoursmay be initialized based on the image processed images and iterativelyoptimized using one or more scale spaces. For example, an image may beinitially processed to locate and isolate one or more regions ofinterest (ROIs). One or more of these ROIs may then be subject to dataproperties standardization filtering to increase signal-to-noise ratio(SNR) and/or to improve contrast between portions of the feature in theimage. After improving SNR and/or contrast in the image, the image issubject to one or more image processing algorithms to enhance sharpnessand/or differentiation and detection of boundaries between portions ofthe feature. The differentiation and detection of the boundaries may beperformed with or without transforming the data into differentrepresentation space, such as Cartesian space or Polar space as may berequired by the specific image processing algorithms implemented. Thedata resulting from the previous processes is then processed to generatea multi-scale data set in Gaussian, geometric, non-linear and/oradaptive shape scale spaces. The multi-scale data set is then used forinitializing a plurality of active contours in the image. The activecontours are then iteratively initialized and optimized in eachresolution level of the one or more space scales allowing for step-wiseoptimization of the active contours. Once optimized, the active contoursidentify and locate all boundaries within the ROI.

The optimized active contours may then form the basis for performingmetrology on desired portions or aspects of the feature in the originalimage or an enhanced image. The metrology may include geometric analysisof segmented regions of the feature based on the contours therein.Further, the metrology may inform other analytical aspects of featureanalysis along with statistical analysis, etc.

Additionally or alternatively, the active contours may be initiatedbased on a separate imaging mode, e.g., multi-modal analysis. Forexample, the sample containing the feature may be analyzed using energydispersive x-ray spectroscopy (EDX) to first determine an initialboundary between portions of the feature, which may then be used aslocations for initializing a respective active contour. The EDX may be asingle line scan across the sample, which will provide at least onepoint on the feature of where the boundary may be. Some samples mayinclude round ROIs, which would then provide two points for eachboundary from the EDX line scan. Of course, a full 2-D EDX scan may alsobe performed, but the time involved may not be desirable.

FIG. 1 is an example image sequence 100 showing image processing andresulting metrology in accordance with an embodiment of the presentdisclosure. The image sequence 100 may be performed on any image types,but will be discussed in the context of images obtained by chargedparticle microscopy. More specifically, but not limiting, the imagesused for illustrating the disclosed techniques are of semiconductorstructures. The image processing and metrology may be performed on anydesired image content and the oval-shaped features of the images in FIG.1 are only for example and are non-limiting to the techniques disclosedherein. For the image sequence 100, the oval-shaped area is a verticalmemory device, sometimes referred to as VNAND. These devices includevarious layers formed in tall vias (e.g., holes formed in the materialthat extend through one or more epi-layers), where each one of thevarious material layers are formed from different materials so that aworking circuit device is formed. For process control and defectdetection, manufacturers of VNAND devices desire measurements of thethickness of the material layers that form the operational device, whichrequires high resolution microscopy, such as SEMs and/or TEMs, toidentify the material layers and their interfaces, e.g., boundariestherebetween.

Image sequence 100 begins with original image 102. While image 102appears to be a dark oval shape surrounded by a lighter colored area,there are actually a number of rings, e.g., material layers, within thedark oval shape, as can be seen in images 108 and 110 for example. Theimage 100 may be a dark field (DF) image or a HAADF image (high angleannular dark field), which conventionally result in the background beinglighter than the regions of interest (ROI). For the image processingsequence, the goal is to determine the location and/or the boundaries ofeach ring within the ROI, and measure the width of one or more of therings, e.g., perform metrology on the VNAND device. To perform thedesired metrology, at least the boundaries between them are to beidentified so that more precise measurements can be taken. To performsuch measurements, however, the image may need to be enhanced and thelocation of the boundaries may need to be more accurately identified.

In general, the sequence may include a pre-processing segment thatenhances the image in terms of signal-to-noise ratio, contrast, regionsharpness, etc., and that also includes extraction/identification of oneor more ROIs. After pre-processing, or image enhancement in general, amulti-scale data set is generated so that active contours can beinitialized and optimized. The optimized active contours will locate theboundaries between the various materials within the image. Based onidentification of the boundaries, metrology of the desired layers orareas in the image may then be performed, which may be performedautomatically.

To begin the image processing, the original image 102 may be analyzed toextract one or more ROIs. The extraction of the ROIs may provide a roughoutside boundary to each of the ROIs and designate an area where theimage processing may be concentrated, e.g., within the rough outsideboundaries. There are many techniques to employ for ROI extraction, suchas binarizing the image to define the outer bounds for one example.Additional techniques will be discussed below. In some embodiments, themeta-information of the original image may also be taken into account inthe image processing. The meta-information including such information asdata type (e.g., imaging mode), resolution, pixel-size, and etc.

After extracting the ROIs, at least that portion of the image is subjectto further processing to enhance the image of the ROI as indicated indashed box 106. In general, the enhancement of the ROI includes reducingthe signal-to-noise ratio, enhancing contrast and image sharpness sothat the boundaries between the different sections/materials are roughlyidentified. For example, image 108 shows the ROI from the original image(with the background removed) with improved contrast. The improvedcontrast image 108 may then be subject to further processing to increasesharpness, as shown by image 110. The image 110 shows the ROI aftervarious filtering operations have been performed, such asreaction-diffusion filtering for example.

Additionally, the image 110 may be used to generate a multi-scale dataset, where different scales at least include Gaussian scale space,geometric scale space, non-linear scale space, and adaptive shape scalespace. The image in each scale space is then subject to a series ofblurring and subsampling to smooth out the image. Such processing isperformed to build potential surfaces for smooth deformation of activecontours.

Further, a model of the ROI is formed to provide bounds of the ROI forfurther processing. For example, various maps of the ROI may begenerated to determine an inner bound and an outer bound used toestablish the area where further image processing is to occur. Anexample map is a distance map of the ROI that determines a center and anoutside edge of the ROI.

After the one or more ROIs of the original image have been enhanced atleast for sharpness and contrast, a large number of active contours maybe initiated within the enhanced ROI and placed in response to thegenerated model of the ROI, where a large number includes tens tohundreds of active contours. A large number of active contours are usedbecause the number of boundaries, e.g., material layers, and theirlocations may not be known a priori. In general, the number ofinitialized active contours will be greater than the number ofboundaries. Image 112 shows the initialization of a plurality of activecontours within the ROI, which may be placed on the original image orthe enhanced version of the original image. The active contours will beallowed to optimize to locate the boundaries, which will coincide withminimum energy locations of the image. Image 114 shows the optimizedactive contours, e.g., snakes. Some of the plurality of active contourswill incorrectly optimize, and will be removed as a result. For example,an incorrectly optimized active contour may relax to differentboundaries within an ROI.

The optimized active contours may then be used as references formeasuring the thickness of different material layers within the ROI, asshown in image 116.

FIG. 2 is an example image sequence 200 showing image processing inaccordance with an embodiment of the present disclosure. The sequence200 is an example image processing sequence that illustrates a differentpart of the overall sequence than that shown in image sequence 100. Ingeneral, each step of the process discussed herein may be performedusing any one or more of image processing algorithms, and the selectionof the algorithms implemented may at least be based on themeta-information of the original image and automatically selected. Theimage sequence 200 begins with original image 202, which, as can beseen, is of different initial quality than image 102. Additionally, theimage 202 may have been obtained using a bright field (BF) image or aTEM imaging mode that produces an image with the background darker thanthe ROI. The VNAND structure of image 202 may be processed to extractthe ROI, the result shown in image 204. As noted above, the extractionof the ROI may generally establish an outer boundary of the ROI.

The extracted ROI of image 204 may then be used as a template forforming a distance map on image 202, e.g., initialize a model of theROI. The distance map is centered on the ROI and can be used to boundthe initial placement of active contours, as shown in image 212.Additionally, the model of the ROI shown in image 218 is used toestablish inner and outer bounds, which are indicated by the inner mostand outer most dashed lines in image 212. The active contours of image212 may be placed on an image that has gone through processing for ROIenhancement, such as shown in box 106 of sequence 100, along with thegeneration of a multi-scale data set that provides energy values toareas within the ROI. The active contours can be placed at increasingdistances from the background, e.g., the area outside of the extractedROI, but within the ROI. Additionally, the model of the ROI shown inimage 218 is used to establish inner and outer bounds, which areindicated by the inner most and outer most dashed lines in image 212.

After the active contours are placed, they are allowed to optimize basedon the multi-scale data set. After optimization of the active contours,metrology may be performed on the layers of the VNAND shown in image202, as shown in box 220.

FIG. 3 is an example method 300 for processing an image and performingmetrology on one or more features within the image in accordance with anembodiment of the present disclosure. The method 300, which is at leastpartially illustrated in FIGS. 1 and 2, may be performed on imagesobtained with a charged particle microscope, such as an SEM, TEM, STEM,to name a few. However, the use of charged particle images is notlimiting on the techniques disclosed herein. Additionally, the method300 may be performed by the imaging tool hardware, one or more serverscoupled to the imaging tool via a network, a user's desktop workstation,or combinations thereof. In general, the method 300 implements one ormore image processing techniques automatically selected from a libraryof techniques to arrive at an image that can be accurately andautonomously measured, e.g., have metrology performed on the one or morefeatures of the image.

The method 300 may begin at process block 301, which includespre-processing the image. In general, the image may experience someprocessing to enhance contrast and sharpness before active contours areused to identify boundaries within an ROI leading to metrology of layersforming those boundaries. The pre-processing may include multipleprocesses to extract the ROI, enhance contrast/region sharpness, improveSNR, and differentiate and detect region boundaries. The imageenhancement can be performed only in the ROI or over the entire image,and is a non-limiting aspect of the present disclosure. Of course,confining the enhancement to within the ROI may improve process time andefficiency of the overall method 300.

Process block 303, an optional sub-step of process block 301, includesextracting a region of interest from an image. The image may be anoriginally obtained image, such as images 102, 202, or may be a croppedportion of an original image. Along with the image data of the inputimage, the process 300 also receives meta-information about the inputimage, where the meta-information includes the data type (e.g., imagingmode), pixel-size, and other data regarding the image. Themeta-information may be used to help automatically determine what imageprocessing techniques to implement in the image processing steps ofmethod 300, e.g., at least steps 301 and 303. The meta informationattached to the image makes the pre-processing filters implemented inprocess block 301 to auto-tune their parameters to the imaging mode ofthe image. Resolution assists in deciding which material layer isaccurately segmented. For example, if the image is of lower resolution,some material layers that are only one or two nanometers thick may nothave enough pixels in them to be resolved accurately. Pixel size, alsorelated to the resolution, may be necessary to automate the process bynormalizing the data to standard pixel size as well as to producemeasurements in standard MKI units rather than pixel units.

There are many image processing techniques that can be used forextracting the ROI. The following includes a non-exclusive list oftechniques that can be implemented, but the technique used is anon-limiting aspect of the present disclosure: linear isotropicdiffusion, histogram manipulation for contrast enhancement, automatedthresholding, component labeling, problem specific size and shapecriteria, partial representation detection and elimination, etc., whichare selected and applied automatically. The extraction of the ROI mayprovide a rough boundary within which the additional image processingand metrology will be performed.

The process block 303 may be followed by process block 305, whichincludes enhancing at least the ROI within the image. The enhancement,in general, is performed to obtain data property standardization and toimprove detection of region boundaries within the ROI. Additionally, theenhancement may improve image contrast, signal-to-noise ratio (SNR),region sharpness, differentiation and detection of region boundaries.The differentiation and detection of region boundaries may be performedwith or without transforming data into different representation spacesuch as Cartesian space or Polar space as may be required by animplemented image filtering technique.

In some embodiments, process block 305 may be split into two processsteps, where contrast and SNR are improved in one step (Step A) andenhancing region sharpness and differentiation and detection of regionboundaries are performed in another step (Step B). Many imagemanipulation algorithms may be selected to implement Step A, such ashistogram manipulation, linear and non-linear contrast enhancement, datanormalization based on local, low-frequency data distribution, gammacorrection, log-correction, brightness correction, etc., where aselected algorithm is applied automatically to at least the ROI of theimage, and selected based on at least the meta information. It should benoted that Step A also performs the data property standardization step.

Likewise, Step B includes selection of one or more algorithms from agroup of similar algorithms that is automatically implemented, basedagain at least on the meta information. The implemented algorithm may beselected from reaction-diffusion filtering, adoptive isotropic andanisotropic diffusion, median filtering, Mumford-Shah model basednon-linear diffusion, background suppression and edge/boundaryextraction, coherence enhancement filtering on the object boundaries,and application of amplitude features, texture (Gabor, Haralick, Laws,LCP, LBP, etc.) techniques. In some embodiments, application of anotherimaging modality, such as energy dispersive x-ray spectroscopy (EDX),electron energy loss spectroscopy (EELS), etc., if available, may beused to differentiate the region boundaries. A more detailed discussionof the use of other imaging modalities is included below.

Process block 301 may be followed by process block 307, which includesgenerating a multi-scale data set of at least the ROI. The multi-scaledata set may be generated in one or more of a number of scale spaces,such as Gaussian scale space, geometric scale space, non-linear scalespace, and adaptive shape scale space. The one or more scale spaces usedto generate the multi-scale data set will be used for optimizing aplurality of active contours. The active contours may be initialized andoptimized on each scale level to determine the boundaries within the ROIon the original scale level. As such, the multi-scale data set will be abasis of optimizing active contours to identify the boundaries betweenthe sections of the ROI.

The process block 307 may be followed by process block 309, whichincludes initializing a model of at least the ROI. Process block 309initializes a general model of the ROI to form first and second boundsof the ROI. The additional processing will mainly be performed withinthe bounds set by the model. The model may be formed based on one ormore techniques chosen from: Binary labelled maps, interactive maps,distance maps, CAD maps, statistical models from the data, Dye cast,random distribution of geometric shapes, geometric models, etc. Thedistance map shown in image 218 provides an example of an initial model.It should be noted that process block 309 can be performed in parallelwith process blocks 305 and 307, and does not need to follow processblock 307. Additionally, the model initialized in process block 307 maybe based on either the original image or the enhanced image.

The process block 309 may be followed by process block 311, whichincludes optimizing a plurality of active contours within the enhancedROI to locate feature boundaries within the enhanced ROI. The process ofoptimizing the active contours, or allowing the active contours tooptimize, may begin with initializing a first plurality of activecontours, where the first plurality is a greater number than the numberremaining after optimization and is also a greater number thanboundaries within the ROI. Initializing more active contours than thereare boundaries may be performed due to the lack of a priori knowledge ofthe number of boundaries within the ROI and/or their location within theROI. While all of the initialized active contours will optimize, somewill likely combine due to optimizing to the same boundary while othersmay be removed due to incorrectly optimizing, e.g., optimizing todifferent boundaries within the ROI. As such, the optimized activecontours will identify and locate the boundaries separating thedifferent sections/materials within the ROI.

The process of initializing and optimizing the active contours may be aniterative process performed at and based on each scale level of themulti-scale data set generated in process block 307. For example, afirst initialization and optimization of active contours may beperformed at a fourth level of scale of the enhanced ROI image, wherethe fourth level of scale has a 1/16^(th) resolution of the originalenhanced ROI image. The active contours that optimize on the fourthlevel of scale image then become the initial active contours on thethird level of scale image, e.g., a ⅛^(th) resolution image, which arethen allowed to optimize. This process is iterated until the originalscale image has had active contours initialized and optimized, thusidentifying and locating the desired boundaries within the ROI.

The process block 311 may be followed by process block 313, whichincludes performing metrology on the ROI within the original image basedon the optimized plurality of active contours. The metrology may providemeasurements of the width of the different sections based on thedistance between different boundaries, and further provide informationon the overall shape of the boundaries. In some embodiments, themetrology may use geometric analysis of the segmented, e.g., identifiedsections, and the contours. Post metrology, the obtained data may beused for statistical inference, hypothesis generation, defect detection,process control, temporal analytics, prediction and other applications.

FIG. 4 is a method 400 for optimizing a plurality of active contourswithin an ROI in accordance with an embodiment of the presentdisclosure. The method 400 can be implemented in conjunction with method300, such as step 311, and may provide one example of the implementationof step 311. The method 400 may begin at process block 401, whichincludes generating a multi-scale data set of an image. The multi-scaledata set will include data sets at multiple resolution levels and foreach of a plurality of scale spaces, such as Gaussian, geometric,non-linear and adaptive shape scale spaces, to name a few. Of course,other scale spaces may also be implemented. The multi-space data set isgenerated from an enhanced image, such as that produced by process block301 of method 300. Using the enhanced image provides better definedboundaries, which are highlighted and smoothed out in the multi-scaledata set. Process block 401 may be followed by process block 403, whichdirects the remainder of method 400 to be completed for each scalespace.

For each scale space, the process blocks 405 and 407 may be performedfor each resolution level. And once all resolution levels of a scalespace are performed, the optimized active contours may be used as theinitial active contours for a subsequent scale space. Of course, that isnot limiting and each scale space may begin with a new set of activecontours.

The process block 403 may be followed by process block 405, whichincludes initializing a plurality of active contours on the ROI. If thisis the initial performance of process block 403 for a scale space, thenthe plurality of active contours will be initialized on a lowestresolution level data. The lowest resolution level may be dependent onthe initial quality of the image but can be 1/16^(th) resolution, orless. The lowest level used, however, is a non-limiting aspect of thepresent disclosure. If this is not the lowest level resolution image,then the initialized active contours will be the optimized activecontours from a lower resolution level image, e.g., a previousiterations of process blocks 405 and 407.

The image of the ROI at the different resolution images in each of thescale spaces may be characterized as having been blurred an amount basedon the level of resolution, where the lower the resolution the moreblurring that occurs. The blurring of the boundaries of the enhancedimage provides a larger energy band for the active contours to optimizein response to. It should also be noted that the higher level data sethas less blur, which results in a more narrow energy band foroptimization. As such, by successively using active contours optimizedat lower resolutions, the active contours are optimized to the fullresolution in a step-wise manner.

The process block 405 may be followed by process block 407, whichincludes optimizing the plurality of active contours. The optimizationof the active contours allows the active contours to move/settle in themiddle of the smoothed out boundaries that each resolution levelprovides. As the process blocks 405 and 407 are iteratively performed,the active contours are optimized in a step-like function and theyeventually optimize to the boundaries in the original resolution imagethus identifying and locating the one or more boundaries in the image.

Process block 407 is followed by process block 409, which determines ifall resolution levels of a scale space are complete. If no, then processblock 405 and 407 are repeated for the next resolution level image. Ifso, then, method 400 proceeds to process block 411, which determines ifall scale spaces have been completed. If not, then the process returnsto process block 403, else it ends at process block 413. The completionof method 400 locates and identifies all boundaries within the ROI ofthe enhanced image, which may then be overlaid or associated withareas/boundaries in the original image.

FIG. 5 is an example image sequence 500 in accordance with an embodimentof the present disclosure. The image sequence 500 illustrates anotherprocess that can be used to initiate and optimize active contours foranchoring metrology. Image 502 includes an original image of a VNANDalong with parts of two other VNANDs. Image 522 shows EDX data on thesame VNAND as shown in image 502. As can be seen image 522, the EDXdata, which provide chemical analysis, shows the changes between thematerials forming the different rings of the VNAND. This chemicalinformation may be used to determine a number of boundaries within theVNAND and a rough location of each of those boundaries.

In some embodiments, the EDX data may be provided by a large area scanas shown in image 522. However, such a large area scan may be replacedby a simple EDX line scan, which is quicker and more efficient. The EDXline scan may be performed across the VNAND, or any imaged structure, toidentify the boundaries of each ring across the diameter of thestructure.

The EDX data of image 522 may then be used to initialize a respectivenumber of active contours at indicated boundaries and place them on theimage 522 according to the location of the boundaries. Once the activecontours are placed, they are allowed to optimize. The optimization ofthe active contours should more accurately identify the location of theboundaries. In some embodiments, the boundaries identified by the EDXdata will be used to place a respective number of active contours foroptimization. For example, if the EDX data show seven boundaries, thenseven active contours will be initialized, with each active contourinitialized at the location of one identified boundary. In otherembodiments, enhanced image data will be used to generate a multi-scaledata set, which then becomes the basis for the initialization andoptimization of the active contours. In such embodiments, however, thenumber and location of the initialized active contours will be based onthe boundaries identified by the EDX data.

As seen in image 514, the optimized active contours may be overlaid onthe original image 502 or on an enhanced version of image 502 to providea basis for performing metrology, as indicated in block 516.

FIG. 6 is an example method 600 in accordance with an embodiment of thepresent disclosure. The method 600 is at least partially illustrated bythe image sequence 500 and includes a different imaging modality thanwhat was used to form an image to assist in determining sectionboundaries and anchoring metrology. While the disclosure uses EDX as thedifferent modality, other modalities may also be used, such as EELS forexample. The method 600 may begin at process block 601, which includesobtaining an image of at least an ROI of a sample. The image may beobtained using an SEM, TEM, STEM, or other imaging technique, which maybe referred to as a first imaging technique. For charged particlemicroscopes, the image may be a gray-scale image based on electronspassing through the sample (e.g., TEM or STEM), secondary electrons(e.g., SEM), and/or backscattered electrons (e.g., SEM). Of course, theimage may be obtained from a light-based microscope as well.

Process block 601 may be followed by process block 603, which includesperforming a second imaging technique on at least the ROI. The secondimaging technique can be any imaging/analytic modality different thanthe first imaging technique. In some embodiments, it may be desirablethat the second imaging technique is a chemical analysis tool, such asEDX. Using EDX as an example second imaging technique, the EDX data willchemically show changes in material. These changes in material providingan approximation of boundaries within the ROI. Using EDX as the secondimaging technique may be performed as either a two-dimensional area scanover all of the ROI, or as a line scan across the ROI.

Process block 603 may be followed by process block 605, which includesinitializing a plurality of active contours within the ROI of theobtained image, the obtained image being obtained using the firstimaging technique. In some embodiments, the obtained image is theoriginally obtained image that has not received any additional imageprocessing. In other embodiments, however, the plurality of activecontours may be initialized on an enhanced image that has been processedto improve contrast, SNR, sharpness, etc. along with undergoing imagingusing the second imaging technique so that the boundaries are moredefined. In yet another embodiment, the plurality of active contours maybe iteratively and recursively initialized and optimized on a series ofresolution adjusted images of one or more scale spaces, as discussedabove. However, while the previous methods, such as method 300 and 400,initialized a larger number of active contours than there wereboundaries, in the method 600, the second imaging technique provides anumber of boundaries that are within the ROI. As such, theinitialization of active contours in method 600 includes initializing arespective number of active contours as there are boundaries identifiedby the second imaging technique. Additionally, the active contoursinitialized in process block 605 will be initialized in a locationdetermined by the second imaging technique data.

Process block 605 may be followed by process block 607, which includesoptimizing the plurality of active contours within the ROI to locatefeature boundaries within the ROI. The Optimization of the activecontours may be performed as discussed previously with regards tomethods 300 and/or 400, but may also be performed based on the secondimaging technique data. Regardless of optimization process, processblock 605 results in identification and location information regardingthe boundaries within the ROI, the boundaries being interfaces betweendifferent materials within the ROI, such as the VNAND of FIG. 5.

Process block 607 is followed by process block 609, which includesperforming metrology on the features within the ROI based on theoptimized plurality of active contours. The metrology providesmeasurements of various aspects of the features within the ROI, such asfeature dimensions, overall shape of the features within the ROI,information regarding process control and/or defects, and otherdesirable measurement-based information.

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), or network processing units (NPUs)that are persistently programmed to perform the techniques, or mayinclude one or more general purpose hardware processors or graphicsprocessing units (GPUs) programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, FPGAs, or NPUs with custom programmingto accomplish the techniques. The special-purpose computing devices maybe desktop computer systems, portable computer systems, handhelddevices, networking devices or any other device that incorporateshard-wired and/or program logic to implement the techniques. In someembodiments, the special-purpose computing device may be a part of thecharged particle microscope or coupled to the microscope and other usercomputing devices.

For example, FIG. 7 is a block diagram that illustrates a computersystem 700 upon which an embodiment of the invention may be implemented.The computing system 700 may be an example of the computing hardwareincluded with the charged particle environment shown in FIG. 8. Computersystem 700 at least includes a bus or other communication mechanism forcommunicating information, and a hardware processor 730 coupled with thebus (not shown) for processing information. Hardware processor 730 maybe, for example, a general purpose microprocessor. The computing system700 may be used to implement the methods and techniques disclosedherein, such as methods 300, 400 and/or 600, and may also be used toobtain images and process said images with one or morefilters/algorithms.

Computer system 700 also includes a main memory 732, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to the busfor storing information and instructions to be executed by processor730. Main memory 732 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 730. Such instructions, when stored innon-transitory storage media accessible to processor 730, rendercomputer system 700 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 700 further includes a read only memory (ROM) 734 orother static storage device coupled to bus 740 for storing staticinformation and instructions for processor 730. A storage device 736,such as a magnetic disk or optical disk, is provided and coupled to bus740 for storing information and instructions.

Computer system 700 may be coupled via the bus to a display, such as acathode ray tube (CRT), for displaying information to a computer user.An input device, including alphanumeric and other keys, is coupled tothe bus for communicating information and command selections toprocessor 730. Another type of user input device is a cursor control,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 730 and forcontrolling cursor movement on the display. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 700 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 700 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 700 in response to processor 730 executing one or more sequencesof one or more instructions contained in main memory 732. Suchinstructions may be read into main memory 732 from another storagemedium, such as storage device 736. Execution of the sequences ofinstructions contained in main memory 732 causes processor 730 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 736.Volatile media includes dynamic memory, such as main memory 732. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge,content-addressable memory (CAM), and ternary content-addressable memory(TCAM).

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise the bus. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Computer system 700 also includes a communication interface 738 coupledto the bus. Communication interface 738 provides a two-way datacommunication coupling to a network link (not shown) that is connectedto a local network, for example. As another example, communicationinterface 738 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 738sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Computer system 700 can send messages and receive data, includingprogram code, through the network(s), a network link and communicationinterface 738. In the Internet example, a server might transmit arequested code for an application program through Internet, ISP, localnetwork and communication interface 738.

The received code may be executed by processor 730 as it is received,and/or stored in storage device 736, or other non-volatile storage forlater

FIG. 8 is an example charged particle microscope environment 800 forperforming at least part of the methods disclosed herein and inaccordance with an embodiment of the present disclosure. The chargedparticle microscope (CPM) environment 800 may include a charged particlemicroscope 850, a network 860, a user station 880, and server(s) 870.The various components of the CPM environment 800 may be co-located at auser's location or distributed. Additionally, some or all of thecomponents will include a computing system, such as the computing system700, for performing the methods disclosed herein. Of course, the CPMenvironment is just an example operating environment for implementingthe disclosed techniques and should not be considered limiting on theimplementation of said techniques.

The CPM 850 may be any type of charged particle microscope, such as aTEM, an SEM, an STEM, a dual beam system, or a focused ion beam (FIB)system. The dual beam system is a combination of an SEM and a FIB, whichallows for both imaging and material removal/deposition. Of course, thetype of microscope is a non-limiting aspect of the present disclosureand the techniques disclosed herein may also be implemented on imagesobtained by other forms of microscopy and imaging. The CPM 850 may beused to obtain images of samples and ROIs included therein forprocessing with the methods disclosed herein. However, the disclosedmethods may be implemented by the CPM environment 800 on images obtainedby other microscopes as well.

The network 860 may be any type of network, such as a local area network(LAN), wide area network (WAN), the internet, or the like. The network860 may be coupled between the other components of the CPM environment800 so that data and processing code may be transmitted to the variouscomponents for implementing the disclosed techniques. For example, auser at user station 880 may receive an image from CPM 850 withintentions of processing in accordance with the disclosed techniques.The user may then retrieve code from server(s) 870, either directly orvia network 860, for performing the image processing and metrology.Else, the user may initiate the process by providing the image to theserver(s) 870, either directly or from the CPM 850 via the network 860,so the processing and metrology is performed by the server(s) 870.

In some examples, values, procedures, or apparatuses are referred to as“lowest”, “best”, “minimum,” or the like. It will be appreciated thatsuch descriptions are intended to indicate that a selection among manyused functional alternatives can be made, and such selections need notbe better, smaller, or otherwise preferable to other selections. Inaddition, the values selected may be obtained by numerical or otherapproximate means and may only be an approximation to the theoreticallycorrect/value.

What is claimed is:
 1. A method comprising: extracting a region ofinterest from an image, the region including one or more boundariesbetween different sections of the region of interest; generating amulti-scale data set of the region of interest based on the region ofinterest; initializing a model of the region of interest, theinitialized model determining at least a first boundary and a secondboundary within the region of interest; optimizing a plurality of activecontours within the region of interest based on the model of the regionof interest and further based on the multi-scale data set, the optimizedplurality of active contours identifying the one or more boundarieswithin the region of interest; and performing metrology on the region ofinterest based on the identified boundaries, wherein performingmetrology on the region of interest comprises automatically performinggeometric analysis of the different sections separated by the one ormore boundaries.
 2. The method of claim 1, further comprising enhancingat least the extracted region of interest based on one or more filters,and wherein the multi-scale data is generated at least in part on theregion of interest.
 3. The method of claim 2, wherein generating amulti-scale data set of the region of interest based on the region ofinterest comprises generating a plurality of image resolution levels ofthe region of interest using one or more scale spaces, wherein the oneor more scale spaces selected from a Gaussian scale space, a geometricscale space, a non-linear scale space, and an adaptive space scalespace.
 4. The method of claim 2, wherein enhancing at least theextracted region of interest based on one or more filters includes:improving a contrast of at least the region of interest within theimage; and improving a signal to noise ratio of at least the region ofinterest within the image.
 5. The method of claim 4, wherein improvingthe contrast and the signal to noise ratio comprises automaticallyselecting and applying the one or more filters, wherein the one or morefilters are selected from histogram manipulation, linear and non-linearcontrast enhancement, data normalization based on local, low-frequencydata distribution, gamma correction, log-correction, and brightnesscorrection.
 6. The method of claim 1, wherein initializing a model ofthe region of interest, the initialized model determining at least firstand second bounds of the region of interest comprises initializing themodel of the region of interest based one or more image maps selectedfrom binary labelled maps, interactive maps, distance maps, CAD maps,statistical models from the data, Dye cast, random distribution ofgeometric shapes, and geometric models.
 7. The method of claim 1,further comprising: improving sharpness of the region of interest;differentiating and detecting the one or more boundaries in the regionof interest; and wherein the model of the region of interest isinitialized based in part on the differentiated and detected one or moreinitializing a model of the region of interest.
 8. A method comprising,extracting a region of interest from an image, the region including oneor more boundaries between different sections of the region of interest;generating a multi-scale data set of the region of interest based on theregion of interest; initializing a model of the region of interest, theinitialized model determining at least a first boundary and a secondboundary within the region of interest; optimizing a plurality of activecontours within the region of interest based on the model of the regionof interest and further based on the multi-scale data set, the optimizedplurality of active contours identifying the one or more boundarieswithin the region of interest, wherein optimizing a plurality of activecontours within the region of interest based on the model of the regionof interest and further based on the multi-scale data set comprises:initializing a first plurality of active contours within the first andsecond bounds of the initialized model; and allowing the first pluralityof active contours to optimize to the plurality of active contours toidentify the one or more boundaries within the region of interest; andperforming metrology on the region of interest based on the identifiedboundaries.
 9. The method of claim 8, wherein there are a greater numberof active contours in the first plurality of active contours than thereare boundaries within the region of interest.
 10. A non-transitory,computer readable medium storing instructions that, when executed by oneor more processors, triggers the one or more processors to perform thestems of: extracting a region of interest from an image, the regionincluding one or more boundaries between different sections of theregion of interest; generating a multi-scale data set of the region ofinterest based on the region of interest; initializing a model of theregion of interest, the initialized model determining at least first andsecond bounds of the region of interest; optimizing a plurality ofactive contours within the region of interest based on the model of theregion of interest and further based on the multi-scale data set, theoptimized plurality of active contours identifying the one or moreboundaries within the region of interest; and performing metrology onthe region of interest based on the identified boundaries, whereinperforming metrology on the region of interest comprises automaticallyperforming geometric analysis of the different sections separated by theone or more boundaries.
 11. The non-transitory, computer readable mediumof claim 10, wherein optimizing a plurality of active contours withinthe region of interest based on the model of the region of interest andfurther based on the multi-scale data set comprises: initializing afirst plurality of active contours within the first and second bounds ofthe initialized model; and allowing the first plurality of activecontours to optimize to the plurality of active contours to identify theone or more boundaries within the region of interest.
 12. Thenon-transitory, computer readable medium of claim 11, wherein there area greater number of active contours in the first plurality of activecontours than there are boundaries within the region of interest. 13.The non-transitory, computer readable medium of claim 10, wherein theinstructions further trigger the processor to cause the performance ofenhancing at least the extracted region of interest based on one or morefilters, and wherein the multi-scale data is generated at least in parton the enhanced region of interest.
 14. The non-transitory, computerreadable medium of claim 13, wherein generating a multi-scale data setof the region of interest based on the enhanced region of interestcomprises generating a plurality of image resolution levels of theenhanced region of interest using one or more scale spaces, wherein theone or more scale spaces selected from a Gaussian scale space, ageometric scale space, a non-linear scale space, and an adaptive spacescale space.
 15. The non-transitory, computer readable medium of claim10, wherein enhancing at least the extracted region of interest based onone or more filters includes: improving a contrast of at least theregion of interest within the image; and improving a signal to noiseratio of at least the region of interest within the image.
 16. Thenon-transitory, computer readable medium of claim 15, wherein improvingthe contrast and the signal to noise ratio comprises automaticallyselecting and applying the one or more filters, wherein the one or morefilters are selected from histogram manipulation, linear and non-linearcontrast enhancement, data normalization based on local, low-frequencydata distribution, gamma correction, log-correction, and brightnesscorrection.
 17. The non-transitory, computer readable medium of claim10, wherein initializing a model of the region of interest, theinitialized model determining at least first and second bounds of theregion of interest comprises initializing the model of the region ofinterest based one or more image maps selected from binary labelledmaps, interactive maps, distance maps, CAD maps, statistical models fromthe data, Dye cast, random distribution of geometric shapes, andgeometric models.
 18. The non-transitory, computer readable medium ofclaim 10, wherein the instructions further trigger the processor tocause the performance of: improving sharpness of the region of interest;differentiating and detecting the one or more boundaries in the regionof interest; and wherein the model of the region of interest isinitialized based in part on the differentiated and detected one or moreinitializing a model of the region of interest.